论文标题
MASNET：提高具有相互注意的暹罗网络的性能，用于遥感变更检测任务
MASNet:Improve Performance of Siamese Networks with Mutual-attention for Remote Sensing Change Detection Tasks
论文作者
论文摘要
暹罗网络广泛用于遥感变更检测任务。香草暹罗网络具有两个相同的特征提取分支，它们具有共享权重，这两个分支独立工作，并且在将即将发送到解码器头之前才融合了特征地图。但是，我们发现在早期阶段的两个特征提取分支之间进行更改检测任务之间的信息至关重要。在这项工作中，我们介绍了具有相互注意力插件的一般暹罗网络相互注意的暹罗网络（MASNET），以便在两个特征提取分支之间交换信息。我们表明，我们的修改提高了暹罗网络在多变更检测数据集上的性能，并且它适用于卷积神经网络和Visual Transformer。
Siamese networks are widely used for remote sensing change detection tasks. A vanilla siamese network has two identical feature extraction branches which share weights, these two branches work independently and the feature maps are not fused until about to be sent to a decoder head. However we find that it is critical to exchange information between two feature extraction branches at early stage for change detection task. In this work we present Mutual-Attention Siamese Network (MASNet), a general siamese network with mutual-attention plug-in, so to exchange information between the two feature extraction branches. We show that our modification improve the performance of siamese networks on multi change detection datasets, and it works for both convolutional neural network and visual transformer.
